Month: July 2013

After Yahoo’s high-profile purchase of Tumblr, when Yahoo CEO Marissa Mayer said that she would “promise not to screw it up,” this is probably not what she had in mind. Devoted users of Tumblr have been watching closely, worried that the cool, web 2.0 image blogging tool would be tamed by the nearly two-decade-old search giant. One population of Tumblr users, in particular, worried a great deal: those that used Tumblr to collect and share their favorite porn. This is a distinctly large part of the Tumblr crowd: according to one analysis, somewhere near or above 10% of Tumblr is “adult fare.”

Now that group is angry. And Tumblr’s new policies, that made them so angry, are a bit of a mess. Two paragraphs from now, I’m going to say that the real story is not the Tumblr/Yahoo incident, or how it was handled, or even why it’s happening. But the quick run-down, and it’s confusing if you’re not a regular Tumblr user. Tumblr had a self-rating system: blogs with “occasional” nudity should self-rate as “NSFW”. Blogs with “substantial” nudity should rate themselves as “adult.” About two months ago, some Tumblr users noticed that blogs rated “adult” were no longer being listed with the major search engines. Then in June, Tumblr began taking both “NSFW” and “adult” blogs out of their internal search results — meaning, if you search in Tumblr for posts tagged with a particular word, sexual or otherwise, the dirty stuff won’t come up. Unless the searcher already follows your blog, then the “NSFW” posts will appear, but not the “adult” ones. Akk, here, this is how Tumblr tried to explain it:

What this meant is that your existing followers of a blog can largely still see your “NSFW” blog, but it would be very difficult for anyone new to find it. David Karp, founder and CEO of Tumblr, dodged questions about it on the Colbert Report, saying only that Tumblr doesn’t want to be responsible for drawing the lines between artistic nudity, casual nudity, and hardcore porn.

Then a new outrage emerged when some users discover that, in the mobile version of Tumblr, some tag searches turn up no results, dirty or otherwise — and not just for obvious porn terms, like “porn,” but also for broader terms, like “gay”. Tumblr issued a quasi-explanation on their blog, which some commentators and users found frustratingly vague and unapologetic.

What’s ironic is that, I suspect, Tumblr and Yahoo are actually trying to find ways to remain permissive when it comes to NSFW content. They are certainly (so far) more permissive than some of their competitors, including Instagram, Blogger, Vine, and Pinterest, all of whom have moved in the last year to remove adult content, make it systematically less visible to their users, or prevent users from pairing advertising with it. The problem here is their tactics.

Media companies, be they broadcast or social, have fundamentally two ways to handle content that some but not all of their users find inappropriate.

First, they can remove some of it, either by editorial fiat or at the behest of the community. This means writing up policies that draw those tricky lines in the sand (no nudity? what kind of nudity? what was meant by the nudity?), and then either taking on the mantle (and sometimes the flak) of making those judgments themselves, or having to decide which users to listen to on which occasions for which reasons.

Second, and this is what Tumblr is trying, is what I’ll call the “checkpoint” approach. It’s by no means exclusive to new media: putting the X-rated movies in the back room at the video store, putting the magazines on the shelf behind the counter, wrapped in brown paper, scheduling the softcore stuff on Cinemax after bedtime, or scrambling the adult cable channel, all depend on the same logic. Somehow the provider needs to keep some content from some people and deliver it to others. (All the while, of course, they need to maintain their reputation as defender of free expression, and not appear to be “full of porn,” and keep their advertisers happy. Tricky.)

To run such a checkpoint requires (1) knowing something about the content, (2) knowing something about the people, and (3) having a defensible line between them.

First, the content. That difficult decision, about what is artistic nudity, what’s casual nudity, and what’s pornographic? It doesn’t go away, but the provider can shift the burden of making that decision to someone else — not just to get it off their shoulders, but sometimes to hand it someone more capable of making it. Adult movie producers or magazine publishers can self-rate their content as pornographic. An MPAA-sponsored board can rate films. There are problems, of course: either the “who are these people?” problem, as in the mysterious MPAA ratings board, or the “these people are self-interested” problem, as when TV production houses rate their own programs. Still, this self-interest can often be congruent with the interests of the provider: X-rated movie producers know that their options may be the back room or not at all, and gain little i pretending that they’re something they’re not.

Next, the people. It may seem like a simple thing, just keeping the dirty stuff on the top shelf and carding people who want to buy it. Any bodega shopkeep can manage to do it. But it is simple only because it depends on a massive knowledge architecture, the driver’s license, that it didn’t have to generate itself. This is a government sponsored, institutional mechanism that, in part, happens to be engaged in age verification. It requires a massive infrastructure for record keeping, offices throughout the country, staff, bureaucracy, printing services, government authorization, and legal consequences for cases of fraud. All that so that someone can show a card and prove they’re of a certain age. (That kind of certified, high-quality data is otherwise hard to come by, as we’ll see in a moment.)

Finally, a defensible line. The bodega has two: the upper shelf and the cash register. The kids can’t reach, and even the tall ones can’t slip away uncarded, unless they’re also interested in theft. Cable services use encryption: the signal is scrambled unless the cable company authorizes it to be unscrambled. This line is in fact not simple to defend: the descrambler used to be in the box itself, which was in the home and, with the right tools and expertise, openable by those who might want to solder the right tab and get that channel unscrambled. This meant there had to be laws against tampering, another external apparatus necessary to make this tactic stick.

Tumblr? Well. All of this changes a bit when we bring it into the world of digital, networked, and social media. The challenges are much the same, and if we notice that the necessary components of the checkpoint are data, we can see how this begins to take on the shape that it does.

The content? Tumblr asked its users to self-rate, marking their blog as “NSFW” or “adult.” Smart, given that bloggers sharing porn may share some of Tumblr’s interest in putting it behind the checkpoint: many would rather flag their site as pornographic and get to stay on Tumblr, than be forbidden to put it up at all. Even flagged, Tumblr provides them what they need: the platform on which to collect content, a way to gain and keep interested viewers. The categories are a little ambiguous — where is the line between “occasional” and “substantial” nudity to be drawn? Why is the criteria only about amount, rather than degree (hard core vs soft core), category (posed nudity vs sexual act), or intent (artistic vs unseemly)? But then again, these categories are always ambiguous, and must always privilege some criteria over others.

The people? Here it gets trickier. Tumblr is not imposing an age barrier, they’re imposing a checkpoint based on desire, dividing those who want adult content from those who don’t. This is not the kind of data that’s kept on a card in your wallet, backed by the government, subject to laws of perjury. Instead, Tumblr has two ways to try to know what a user wants: their search settings, and what they search for. If users have managed to correctly classify themselves into “Safe Mode,” indicating in the settings that they do not want to see anything flagged as adult, and people posting content have correctly marked their content as adult or not, this should be an easy algorithmic equation: “safe” searcher is never shown “NSFW” content. The only problems would be user error: searchers who do not set their search settings correctly, and posters who do not flag their adult content correctly. Reasonable problems, and the kind of leakage that any system of regulation inevitably faces. Flagging at the blog level (as opposed to flagging each post as adult or not) is a bit of a dull instrument: all posts from my “NSFW” blog are being withheld from safe searchers, even the ones that have no questionable content — despite the fact that by their own definition a “NSFW” tumblr blog only has “occasional” nudity. Still, getting people to rate every post is a major barrier, few will do so diligently, and it doesn’t fit into simple “web button” interfaces.

Defending the dividing line? Since the content is digital, and the information about content and users is data, it should not be surprising that the line here is algorithmic. Unlike the top shelf or the back room, the adult content on Tumblr lives amidst the rest of the archive. And there’s no cash register, which means that there’s no unavoidable point at which use can be checked. There is the login, which explains why non-logged-in users are treated as only wanting “safe” content. But, theoretically, an “algorithmic checkpoint” should work based on search settings and blog ratings. As a search happens, compare the searcher’s setting with the content’s rating, and don’t deliver the dirty to the safe.

But here’s where Tumblr took two additional steps, the ones that I think raise the biggest problem for the checkpoint approach in the digital context.

Tumblr wanted to extend the checkpoint past the customer who walks into the store and brings adult content to the cash register, out to the person walking by the shop window. And those passersby aren’t always logged in, they come to Tumblr in any number of ways. Because here’s the rub with the checkpoint approach: it does, inevitably, remind the population of possible users, that you do allow the dirty stuff. The new customer who walks into the video store, and sees that there is a back room, even if the never go in, may reject your establishment for even offering it. Can the checkpoint be extended, to decide whether to even reveal to someone that there’s porn available inside? If not in the physical world, maybe in the digital?

Tumblr also apparently wanted to extend the checkpoint in the mobile environment — or perhaps were required to, by Apple. Many services, especially those spurred or required by Apple to do so, aim to prevent the “accidental porn” situation: if I’m searching for something innocuous, can they prevent a blast of unexpected porn in response to my query? To some degree, the “NSFW” rating and the “safe” setting should handle this, but of course content that a blogger failed (or refused) to flag still slips through. So Tumblr (and other sites) institute a second checkpoint: if the search term might bring back adult content, block all the results for that term. In Tumblr, this is based on tags: bloggers add tags that describe what they’ve posted, and search queries seek matches in those tags.

When you try to choreograph users based on search terms and tags, you’ve doubled your problem. This is not clean, assured data like a self-rating of adult content or the age on a driver’s license. You’re ascertaining what the producer meant when they tagged a post using a certain term, and what the searcher meant when they use the same term as a search query. If I search for the word “gay,” I may be looking for a gay couple celebrating the recent DOMA decision on the steps of the Supreme Court — or “celebrating” bent over the arm of the couch. Very hard for Tumblr to know which I wanted, until I click or complain.

Sometimes these terms line up quite well, either by accident, or on purpose: for instance when users of Instagram indicated pornographic images by tagging them “pornstagram,” a made-up word that would likely mean nothing else. (This search term no longer returns any results, although — whoa! — it does on Tumblr!.) But in just as many cases, when you use the word gay to indicate a photo of your two best friends in a loving embrace, and I use the word gay in my search query to find X-rated pornography, it becomes extremely difficult for the search algorithm to understand what to do about all of those meanings converging on a single word.

Blocking all results to the query “gay,” or “sex”, or even “porn” may seem, form one vantage point (Yahoo’s?), to solve the NSFW problem. Tumblr is not alone in this regard: Vine and Instagram return no results to the search term “sex,” though that does not mean that no one’s using it as a tag – though Instagram returns millions of results for “gay,” Vine, like Tumblr, returns none. Pinterest goes further, using the search for “porn” as a teaching moment: it pops up a reminder that nudity is not permitted on the site, then returns results which, because of the policy, are not pornographic. By blocking search terms/tags, no porn accidentally makes it to the mobile platform or to the eyes of its gentle user. But, this approach fails miserably at getting adult content to those that want it, and more importantly, in Tumblr’s case, it relegates a broadly used and politically vital term like “gay” to the smut pile.

Tumblr’s semi-apology has begun to make amends. The two categories, “NSFW” and “adult” are now just “NSFW” and the blogs masked as such are now available in Tumblr’s internal search and in the major search engines. Tumblr has promised to work on a more intelligent filtering system. But any checkpoint that depends on data that’s expressive rather than systemic — what we say, as opposed to what we say we are — is going to step clumsily both on the sharing of adult content and the ability to talk about subjects that have some sexual connotations, and could architect the spirit and promise out of Tumblr’s publishing platform.

June 21, 2013 Facebook reported that a bug had potentially exposed 6 million Facebook users’ contact details. While this security breach is a huge at any scale and raises concerns regarding online privacy what I want to bring forward is that it also illuminates how our data is currently used by social media sites. In fact, it is quite interesting that instead of technical description of what happened Facebook wants to tell us why and how it happened:

When people upload their contact lists or address books to Facebook, we try to match that data with the contact information of other people on Facebook in order to generate friend recommendations. For example, we don’t want to recommend that people invite contacts to join Facebook if those contacts are already on Facebook; instead, we want to recommend that they invite those contacts to be their friends on Facebook.

Because of the bug, some of the information used to make friend recommendations and reduce the number of invitations we send was inadvertently stored in association with people’s contact information as part of their account on Facebook. As a result, if a person went to download an archive of their Facebook account through our Download Your Information (DYI) tool, they may have been provided with additional email addresses or telephone numbers for their contacts or people with whom they have some connection. This contact information was provided by other people on Facebook and was not necessarily accurate, but was inadvertently included with the contacts of the person using the DYI tool.

The point I want to focus on here is that in response to the security breach Facebook gives us a rather rare view of how they use user information to establish and maintain user engagement. What is important in this regard is the notion that users’ ‘contact lists’ and ‘address books’ are not only stored to the server but also actively used by Facebook to build new connections and establish new attachments. In this very case your contact details are used to make friend recommendations.

According to Mark Coté and Jennifer Pybus (2007, 101) social networks have an inbuilt “architecture of participation.” This architecture invites users to use the site and then exploits the data user submits to intensify the personalized user experiences. Friend recommendation system is without a doubt a part of these architectures. It is based on the idea that you do not connect with random people but with the people you know. You do not need to search for these people, Facebook suggests them for you with its algorithmic procedures (Bucher 2012). Your real life acquaintances become your Friends on Facebook and you do not have to leave the site to maintain these relationships.

To paraphrase José van Dijck (2013, 12 n9) social media sites engineer our sociality: in other words social media sites are “trying to exert influence on or directing user behavior.” Engineering of sociality needs not to refer to political propaganda or ideological brainwash but can as well be interpreted as technology of keeping users engaged with social media sites. Facebook of course needs user engagement in order to remain productive and to be credible for its shareholders. To be clear, user engagement here is not only emotional or psychological relation to a social media site but a relation that is in extensive manner coded and programmed to the technical and social uses of the platform itself. As such it needs to be researched from views that take into account both human and non-human agencies.

In short, being engaged with social media is a relation of connecting and sharing, discovering and learning, expressing oneself. These architectures of participation work in a circular logic. The more information you provide to social media sites, either explicitly or implicitly (see Schäfer 2009), the more engaged you become. Not only because these sites are able to better place you to a demographic slot based on big data but also because they use the small data, your private data, to personalize the experience. Eventually, you are so engaged that things like compromising the privacy of 6 million users does not stop you from using these sites.

There is this thing called the firehose. I’ve witnessed mathematicians, game theorists, computer scientist and engineers (apparently there is a distinction), economists, business scholars, and social scientist salivate over it (myself included). The Firehouse, though technically reserved for the twitter API, is all encompassing in the realm of social science for the streams of data that come from social networking sites that are so large that they cannot be processed as they come in. The data are so large, in fact, that coding requires multiple levels of computer aided refinement, as though when we take data from these sources we are drinking from a firehose. While I cannot find the etymology of where the term came from, it seems it either came from twitter terminology bleed, or a water fountain at MIT.

I am blessed with an advisor who has become the little voice that I always have at the back of my head when I am thinking about something. Every meeting he asks the same question, one that should be easy to answer but almost never is, especially when we are invested in a topic, “why does this matter?” To date, outside of business uses or artistic exploration we’ve not made a good case for why big data matters. I think we all want it because we think some hidden truth might be within it. We fetishize big data, and the Firehouse that exists behind locked doors, as though it will be the answer to some bigger question. The problem with this is, there is no question. We, from our own unique, biased, and disciplinary homes, have to come up with the bigger questions. We also have to accept that while data might provide us with some answers, perhaps we should be asking questions that go deeper than that in a research practice that requires more reflexivity than we are seeing right now. I would love to see more nuanced readings that acknowledge the biases, gaps, and holes at all levels of big data curation.

Predictive Power of Patterns

One of my favorite anecdotes that shows the power of big data is the Target incident from February 2012. Target predicted a teenage girl was pregnant and acted as such before she told her family. They sent baby centric coupons to her. Her father called Target very angry then called back later to apologize because there were some things his daughter hadn’t told him. The media storm following the event painted a world both in awe and creeped out by Targets predictive power. How could a seemingly random bit of shopping history point to a pattern that showed that a customer was pregnant? How come I hadn’t noticed that they were doing this to me too? Since the incident went public, and Target shared how they learned how to hide the targeted ads and coupons to minimize the creepy factor I’ve enjoyed receiving the Target coupon books that always come in pairs to my home, one for me and one for my husband, that look the same on the surface but have slight variations on the inside. Apparently target has learned that it the coupons for me go to him they will be used. This is because every time I get my coupon books I complain to him about my crappy coupon for something I need. He laughs at me and shows me his coupon, usually worth twice as much as mine if I just spend a little bit more. It almost always works.

In 2004 Lou Agosta wrote a piece titled “The Future of Data Mining- Predictive Analytics”. With the proliferation of social media, API data access, and the beloved yet mysterious firehose, I think we can say the future is now. Our belief and cyclical relationship with progress as a universal future inevitability turns big data into a universal good. While I am not denying the usefulness of finding predictive patterns, clearly Target knew the girl was pregnant and was able to capitalize on that knowledge, for the social scientist, this pattern identification for outcome prediction followed by verification should not be enough. Part of our fetishization of big data seems to be in the idea that somehow it will allow us to not just anticipate, but to know, the future. Researchers across fields and industries are working on ways to extract meaningful, predictive data from these nearly indigestible datastreams. We have to remember that even in big data there are gaps, holes, and disturbances. Rather than looking at what big data can tell us, we should be looking towards it as an exploratory method that can help us define different problem sets and related questions.

Big Data as Method?

Recently I went to a talk by a pair of computer scientists. There were people speaking who had access to the entire database of Wikipedia. Because they could, they decided to visualize Wikipedia. After going through slide after slide of pretty colors, they said “who knew there were rainbows in Wikipedia!?”, and then announced that they had moved on from that research. Rainbows can only get me so far. I was stuck asking why this pattern kept repeating itself and wanting to know how people who were creating the data that turned into a rainbow imagined what they were producing. The visualizations didn’t answer anything. If anything, they allowed me to ask clearer, more directed questions. This isn’t to say the work that they did wasn’t beautiful. It is and was. But there is so much more work to do. I hope that as big data continues to become something of a social norm that more people begin to speak across the lines so that we learn how to use this data in meaningful ways everywhere. Right now I think that visualization is still central, but that is one of my biases. The reason I think this is the case because it allows for simple identification of patterns. It also allows us to take in petabytes of data at once, compare different datasets (if similar visualization methods are used) and, to experiment in a way that other forms of data representation do not. When people share visualizations they either show their understandable failure or the final polished product meant for mass consumption. I’ve not heard a lot of conversation about using big data, its curation, and visualization generation as/and method, but maybe I’m not in the right circles? Still, I think until we are willing to share the various steps along the way to turning big data into meaningful bits, or we create an easy to use toolkit for the next generation of big data visualizations, we will continue to all be hacking at the same problem, ending and stopping at different points, without coming to a meaningful point other than “isn’t big data beautiful?”

This morning, the print version of the New York Times profiled the Kickstarter-funded game “Data Dealer.” The game is a browser-based single-player farming-style clicker with a premise that the player “turns data into cash” by playing the role of a behind-the-scenes data aggregator probably modeled on a real company like Axciom.

Currently there is only a demo, but the developers have big future ambitions, including a multi-player version. Here’s a screen shot:

Data Dealer screen shot (click to enlarge.)

One reason Data Dealer is receiving a lot of attention is that there really isn’t anything else like it. It reminds me of the ACLU’s acclaimed “Ordering Pizza” video (now quite old) which vividly envisioned a dystopian future of totally integrated personal data through the lens of placing orders for pizza. The ACLU video shows you the user interface for a hypothetical software platform built to allow the person who answers the phone at an all-knowing pizza parlor to enter your order.

(In the video, a caller tries to order a “double meat special” and is told that there will be an additional charge because of his high-blood pressure and high cholesterol. He complains about the high price and is told, “But you just bought those tickets to Hawaii!”)

The ACLU video is great because it uses a silly hook to get across some very important societal issues about privacy. It makes a topic that seems very boring — data protection and the risks involved in the interconnection of databases — vivid and accessible. As a teacher working with these issues, I still find the video useful today. Although it looks like the pizza ordering computer is running Windows 95.

Data Dealer has the same promise, but they’ve made some unusual choices. The ACLU’s goal was clearly public education about legal issues, and I think that the group behind Data Dealer has a similar goal. On their Kickstarter profile they describe themselves as “data rights advocates.”

Yet some of the choices made in the game design seem indefensible, as they might create awareness about data issues but they do so by promulgating misguided ideas about how data surveillance actually works. I found myself wondering: is it worth raising public awareness of these issues if they are presented in a way that is so distorted?

As a data aggregator, the chief antagonist in the demo is public opinion. While clearly that would be an antagonist for someone like Axciom, there are actually real risks to data aggregation that involve quantifiable losses. Data protection laws don’t exist solely because people are squeamish.

By focusing on public opinion, the message I am left with isn’t that privacy is really important, it is that “some people like it.” Those darn privacy advocates sure are fussy! (They periodically appear, angrily, in a pop-up window.) This seems like a much weaker argument than “data rights advocates” should be making. It even feels like the makers of Data Dealer are trying to demean themselves! But maybe this was meant to be self-effacing.

I commend Data Dealer for grappling with one of the hardest problems that currently exists in the study of the social implications of computing: how to visualize things like algorithms and databases comprehensibly. In the game, your database is cleverly visualized as a vaguely vacuum-cleaner-like object. Your network is a kind of octopus-like shape. Great stuff!

However, some of the meatiest parts of the corporate data surveillance infrastructure go unmentioned, or are at least greatly underemphasized. How about… credit cards? Browser cookies? Other things are bizarrely over-emphasized relative to the actual data surveillance ecology: celebrity endorsements, online personality tests, and poster ad campaigns.

Algorithms are not covered at all (unless you count the “import” button that automatically “integrates” different profiles into your database.) That’s a big loss, as the model of the game implies that things like political views are existing attributes that can be harvested by (for instance) monitoring what books you buy at a bookstore. The bookstores already hold your political views in this model, and you have to buy them from there. That’s not AT ALL how political views are inferred by data mining companies, and this gameplay model falsely creates the idea that my political views remain private if I avoid loyalty cards in bookstores.

A variety of the causal claims made in the game just don’t work in real life. A health insurance company’s best source for private health information about you is not mining online dating profiles for your stated weight. By emphasizing these unlikely paths for private data disclosure, the game obscures the real process and seems to be teaching those concerned about privacy to take useless and irrelevant precautions.

The crucial missing link is the absence of any depiction of the combination of disparate data to produce new insights or situations. That’s the topic the ACLU video tackles head-on. Although the game developers know that this is important (integration is what your vacuum-cleaner is supposed to be doing), that process doesn’t exist as part of the gameplay. Data aggregation in the game is simply shopping for profiles from a batch of blue sources and selling them to different orange clients (like the NSA or a supermarket chain). Yet combination of databases is the meat of the issue.

By presenting the algorithmic combination of data invisibly, the game implies that a corporate data aggregator is like a wholesaler that connects suppliers to retailers. But this is not the value data aggregation provides, that value is all about integration.

Finally, the game is strangely interested in the criminal underworld, promoting hackers as a route that a legitimate data mining corporation would routinely use. This is just bizarre. In my game, a real estate conglomerate wanted to buy personal data so I gathered it from a hacker who tapped into an Xbox Live-like platform. I also got some from a corrupt desk clerk at a tanning salon. This completely undermines the game as a corporate critique, or as educational.

In sum, it’s great to see these hard problems tackled at all, but we deserve a better treatment of them. To be fair, this is only the demo and it may be that the missing narratives of personal data will be added. A promised addition is that you can create your own social media platform (Tracebook) although I did not see this in my demo game. I hope the missing pieces are added. (It seems more unlikely that the game’s current flawed narratives will be corrected.)

My major reaction to the game is that this situation highlights the hard problems that educational game developers face. They want to make games for change, but effective gameplay and effective education are such different goals that they often conflict. For the sake of a salable experience the developers here clearly felt they had to stake their hopes on the former and abandon the latter, abandoning reality.

More than a million Brazilians have joined protests in over 100 cities throughout Brazil in the past few weeks. Since their early beginning as a “Revolta do Busão” (Bus rebellion) to reduce bus fares, the protests now include a much larger set of issues faced by Brazilian society. Protesters are angry about corruption and inequality. They’re also frustrated about the cost of hosting the upcoming World Cup and Olympic Games in light of economic disparity and lack of high quality basic services. Yesterday, as Brazil defeated Spain to win the Confederations Cup final, police clashed with protesters near Maracana stadium for the second time in two weeks.

People turned to social media to share what they saw on the streets and invite others to join in the protests. For example, some of our most active Brazilian users of So.cl have been posting daily collages with images, links, and descriptions of the protests. According to a well-known polling company, a surprising 72% of Brazilians online supported the demonstrations, and 10% claimed to have joined the protests on the streets. For a while, leftist President Rouseff maintained a high approval rate of 55%, down from 63% the year before and still one of the highest for any leader in the world. By June 29th, however, only only 30% of Brazilians considered her administration “great” or “good.”

About Us

The Social Media Collective (SMC) is a network of social science and humanistic researchers, part of the Microsoft Research labs in New England and New York. It includes full-time researchers, postdocs, interns, and visitors. Beginning in 2009, the researchers who now lead the initiative are: Nancy Baym, danah boyd, Kate Crawford, Tarleton Gillespie, and Mary Gray. Our primary purpose is to provide rich contextual understanding of the social and cultural dynamics that underpin social media technologies. We use a variety of methodologies and span multiple disciplines.

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